This notebook illustrates using clustering and our descriptors compounds.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.offline as pyo
import plotly.graph_objs as go
pyo.init_notebook_mode()
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score
from yellowbrick.cluster import KElbowVisualizer
import warnings
warnings.filterwarnings("ignore")
# import data
data = pd.read_csv("../../../data/processed/drug_bank_clean.csv")
data.head(2)
| Name | SMILES | ALogP | ALogp2 | AMR | apol | nAcid | naAromAtom | nAromBond | nAtom | ... | MW | WTPT-1 | WTPT-2 | WTPT-3 | WTPT-4 | WTPT-5 | WPATH | WPOL | XLogP | Zagreb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Bivalirudin | CC[C@H](C)[C@H](NC(=O)[C@H](CCC(O)=O)NC(=O)[C@... | -5.0931 | 25.939668 | 534.3813 | 317.363434 | 6 | 18 | 18 | 293 | ... | 2180.289181 | 306.516036 | 1.977523 | 153.54308 | 82.176355 | 71.366725 | 286334.0 | 230.0 | -8.623 | 752.0 |
| 1 | Leuprolide | CCNC(=O)[C@@H]1CCCN1C(=O)[C@H](CCCNC(N)=N)NC(=... | 0.4325 | 0.187056 | 317.0896 | 187.074612 | 0 | 20 | 21 | 171 | ... | 1209.400552 | 174.200943 | 2.002310 | 78.03037 | 30.548034 | 47.482336 | 46759.0 | 130.0 | 1.134 | 436.0 |
2 rows × 224 columns
# getting all descriptors except compound name
without_compound = data.drop(['Name','SMILES'], axis=1)
without_compound.head(2)
| ALogP | ALogp2 | AMR | apol | nAcid | naAromAtom | nAromBond | nAtom | ATSc1 | ATSc2 | ... | MW | WTPT-1 | WTPT-2 | WTPT-3 | WTPT-4 | WTPT-5 | WPATH | WPOL | XLogP | Zagreb | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -5.0931 | 25.939668 | 534.3813 | 317.363434 | 6 | 18 | 18 | 293 | 5.125258 | -2.388694 | ... | 2180.289181 | 306.516036 | 1.977523 | 153.54308 | 82.176355 | 71.366725 | 286334.0 | 230.0 | -8.623 | 752.0 |
| 1 | 0.4325 | 0.187056 | 317.0896 | 187.074612 | 0 | 20 | 21 | 171 | 2.269431 | -1.093604 | ... | 1209.400552 | 174.200943 | 2.002310 | 78.03037 | 30.548034 | 47.482336 | 46759.0 | 130.0 | 1.134 | 436.0 |
2 rows × 222 columns
# scaling our data by using standard scaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(without_compound)
# Instantiate the KMeans model
kmeans_model = KMeans()
# Instantiate the KElbowVisualizer
elbow_visualizer = KElbowVisualizer(kmeans_model, k=(2, 10))
# Fit the visualizer to the data
elbow_visualizer.fit(X_scaled)
# Visualize the elbow plot
elbow_visualizer.show()
<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>
sns.set()
# elbow method
n_clusters = 20
cost = []
for i in range(2, n_clusters):
kmean= KMeans(i, n_init=10, max_iter=1000)
kmean.fit(X_scaled)
labels = kmean.labels_
cost.append(kmean.inertia_)
print("Cluster {} Sillhoute Score {}".format(i, silhouette_score(without_compound, labels)))
plt.title("Elbow Method")
plt.plot(cost, 'bx-');
Cluster 2 Sillhoute Score 0.5834159976927242 Cluster 3 Sillhoute Score 0.4619443890753173 Cluster 4 Sillhoute Score 0.3260162289955886 Cluster 5 Sillhoute Score 0.3375511220996368 Cluster 6 Sillhoute Score 0.22889785175640734 Cluster 7 Sillhoute Score 0.2179995116659977 Cluster 8 Sillhoute Score 0.19405851201962937 Cluster 9 Sillhoute Score 0.18827055457714917 Cluster 10 Sillhoute Score 0.19000213056913812 Cluster 11 Sillhoute Score 0.08623036110180443 Cluster 12 Sillhoute Score 0.10108716322128757 Cluster 13 Sillhoute Score 0.06277159205146708 Cluster 14 Sillhoute Score 0.01811190398429743 Cluster 15 Sillhoute Score 0.059182994555299 Cluster 16 Sillhoute Score -0.06234871662307226 Cluster 17 Sillhoute Score 0.07262932898934762 Cluster 18 Sillhoute Score -0.014300273482830523 Cluster 19 Sillhoute Score -0.006581185234594802
kmean = KMeans(4, n_init=10, max_iter=1000)
kmean.fit(X_scaled)
labels = kmean.labels_
# adding clusters to our data
clusters = pd.concat([without_compound, pd.DataFrame({'cluster': labels})], axis=1)
clusters.head()
| ALogP | ALogp2 | AMR | apol | nAcid | naAromAtom | nAromBond | nAtom | ATSc1 | ATSc2 | ... | WTPT-1 | WTPT-2 | WTPT-3 | WTPT-4 | WTPT-5 | WPATH | WPOL | XLogP | Zagreb | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -5.0931 | 25.939668 | 534.3813 | 317.363434 | 6 | 18 | 18 | 293 | 5.125258 | -2.388694 | ... | 306.516036 | 1.977523 | 153.543080 | 82.176355 | 71.366725 | 286334.0 | 230.0 | -8.623 | 752.0 | 3 |
| 1 | 0.4325 | 0.187056 | 317.0896 | 187.074612 | 0 | 20 | 21 | 171 | 2.269431 | -1.093604 | ... | 174.200943 | 2.002310 | 78.030370 | 30.548034 | 47.482336 | 46759.0 | 130.0 | 1.134 | 436.0 | 3 |
| 2 | -1.8743 | 3.513000 | 325.6625 | 190.878612 | 0 | 20 | 21 | 175 | 2.635543 | -1.251529 | ... | 181.809125 | 1.997902 | 88.729976 | 35.878826 | 52.851150 | 52357.0 | 134.0 | -2.012 | 458.0 | 3 |
| 3 | 9.1843 | 84.351366 | 493.8733 | 292.709055 | 0 | 36 | 40 | 266 | 3.021552 | -1.418733 | ... | 261.668224 | 1.997467 | 98.311965 | 40.550947 | 57.761019 | 148212.0 | 208.0 | 8.583 | 660.0 | 3 |
| 4 | -2.8933 | 8.371185 | 269.4076 | 154.458752 | 0 | 12 | 12 | 138 | 2.148910 | -0.961833 | ... | 147.352827 | 1.991254 | 75.875569 | 30.211599 | 39.611320 | 28498.0 | 110.0 | -1.437 | 360.0 | 3 |
5 rows × 223 columns
silhouette_score(without_compound, labels)
0.32077577396105217
# using pca with 2 components to visualize our scaled data with clusters
components = 2
# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_scaled)
# making dataframe for our pca components
pca_df = pd.DataFrame(X_pca, columns=['PCA1','PCA2'])
# adding cluster assignment
pca_df['cluster'] = pd.Categorical(kmean.labels_)
# adding compound name to this dataframe
# pca_df['compound'] = data['Name']
# get the index of the most important feature on EACH component i.e. largest absolute value
most_important = [np.abs(pca.components_[i]).argmax() for i in range(components)]
# columns/ features of our data
initial_feature_names = without_compound.columns
# get the names
most_important_names = [initial_feature_names[most_important[i]] for i in range(components)]
# using LIST COMPREHENSION HERE AGAIN
dic = {'PC{}'.format(i + 1): most_important_names[i] for i in range(components)}
# explained variance
print("Explained variance ratio", pca.explained_variance_ratio_)
print("\nnSum of Explained variance ratio", pca.explained_variance_ratio_.sum())
print("\nColumns chosen by PCA", dic)
pca_df
# Adjust the figure size
plt.figure(figsize=(12, 8))
# Create the scatterplot using Seaborn
sns.scatterplot(data=pca_df, x='PCA1', y='PCA2', hue="cluster")
# Set the plot title
plt.title("Compounds Clustering - All Features")
# Show the plot
plt.show()
# fig = px.scatter(pca_df, x='PCA1', y='PCA2', color="cluster", title="Compounds Clustering - All Features", width=800)
# fig.show()
# saving the figure in html file
# fig.write_html("cluster4.html")
Explained variance ratio [0.34087863 0.09107553]
nSum of Explained variance ratio 0.4319541614805529
Columns chosen by PCA {'PC1': 'Zagreb', 'PC2': 'khs.ssssC'}
for c in clusters:
grid = sns.FacetGrid(clusters, col="cluster")
grid.map(plt.hist, c)
# using pca with 2 components to visualize our scaled data with clusters
components = 0.99
# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_scaled)
pca.n_components_
82
kmean = KMeans(4, n_init=10, max_iter=1000)
kmean.fit(X_pca)
labels = kmean.labels_
silhouette_score(without_compound, labels)
0.3260162289955886
# using pca with 2 components to visualize our scaled data with clusters
components = 2
# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_pca)
# making dataframe for our pca components
pca_df = pd.DataFrame(X_pca, columns=['PCA1','PCA2'])
# adding cluster assignment
pca_df['cluster'] = pd.Categorical(kmean.labels_)
# adding compound name to this dataframe
# pca_df['compound'] = data['Name']
# get the index of the most important feature on EACH component i.e. largest absolute value
most_important = [np.abs(pca.components_[i]).argmax() for i in range(components)]
# columns/ features of our data
initial_feature_names = without_compound.columns
# get the names
most_important_names = [initial_feature_names[most_important[i]] for i in range(components)]
# using LIST COMPREHENSION HERE AGAIN
dic = {'PC{}'.format(i + 1): most_important_names[i] for i in range(components)}
# explained variance
print("Explained variance ratio", pca.explained_variance_ratio_)
print("\nnSum of Explained variance ratio", pca.explained_variance_ratio_.sum())
print("\nColumns chosen by PCA", dic)
pca_df
# Adjust the figure size
plt.figure(figsize=(12, 8))
# Create the scatterplot using Seaborn
sns.scatterplot(data=pca_df, x='PCA1', y='PCA2', hue="cluster")
# Set the plot title
plt.title("Compounds Clustering - All Features")
# fig = px.scatter(pca_df, x='PCA1', y='PCA2', color="cluster", title="Compounds Clustering - 99% Components PCA", width=800)
# # saving the figure in html file
# # fig.write_html("cluster4.html")
# fig.show()
Explained variance ratio [0.34426944 0.09198149]
nSum of Explained variance ratio 0.43625092682357886
Columns chosen by PCA {'PC1': 'ALogP', 'PC2': 'ALogp2'}
Text(0.5, 1.0, 'Compounds Clustering - All Features')
# adding clusters to our data
clusters = pd.concat([without_compound, pd.DataFrame({'cluster': labels})], axis=1)
clusters.head(3)
| ALogP | ALogp2 | AMR | apol | nAcid | naAromAtom | nAromBond | nAtom | ATSc1 | ATSc2 | ... | WTPT-1 | WTPT-2 | WTPT-3 | WTPT-4 | WTPT-5 | WPATH | WPOL | XLogP | Zagreb | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -5.0931 | 25.939668 | 534.3813 | 317.363434 | 6 | 18 | 18 | 293 | 5.125258 | -2.388694 | ... | 306.516036 | 1.977523 | 153.543080 | 82.176355 | 71.366725 | 286334.0 | 230.0 | -8.623 | 752.0 | 3 |
| 1 | 0.4325 | 0.187056 | 317.0896 | 187.074612 | 0 | 20 | 21 | 171 | 2.269431 | -1.093604 | ... | 174.200943 | 2.002310 | 78.030370 | 30.548034 | 47.482336 | 46759.0 | 130.0 | 1.134 | 436.0 | 3 |
| 2 | -1.8743 | 3.513000 | 325.6625 | 190.878612 | 0 | 20 | 21 | 175 | 2.635543 | -1.251529 | ... | 181.809125 | 1.997902 | 88.729976 | 35.878826 | 52.851150 | 52357.0 | 134.0 | -2.012 | 458.0 | 3 |
3 rows × 223 columns
for c in clusters:
grid = sns.FacetGrid(clusters, col="cluster")
grid.map(plt.hist, c)
# using pca with 2 components to visualize our scaled data with clusters
components = 2
# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_scaled)
pca.n_components_
2
kmean = KMeans(4, n_init=10, max_iter=1000)
kmean.fit(X_pca)
labels = kmean.labels_
silhouette_score(without_compound, labels)
0.3392598203636521
# using pca with 2 components to visualize our scaled data with clusters
components = 2
# initilizing and fitting the pca
pca = PCA(n_components=components)
X_pca = pca.fit_transform(X_pca)
# making dataframe for our pca components
pca_df = pd.DataFrame(X_pca, columns=['PCA1','PCA2'])
# adding cluster assignment
pca_df['cluster'] = pd.Categorical(kmean.labels_)
# adding compound name to this dataframe
# pca_df['compound'] = data['Name']
# get the index of the most important feature on EACH component i.e. largest absolute value
most_important = [np.abs(pca.components_[i]).argmax() for i in range(components)]
# columns/ features of our data
initial_feature_names = without_compound.columns
# get the names
most_important_names = [initial_feature_names[most_important[i]] for i in range(components)]
# using LIST COMPREHENSION HERE AGAIN
dic = {'PC{}'.format(i + 1): most_important_names[i] for i in range(components)}
# explained variance
print("Explained variance ratio", pca.explained_variance_ratio_)
print("\nnSum of Explained variance ratio", pca.explained_variance_ratio_.sum())
print("\nColumns chosen by PCA", dic)
pca_df
# Adjust the figure size
plt.figure(figsize=(12, 8))
# Create the scatterplot using Seaborn
sns.scatterplot(data=pca_df, x='PCA1', y='PCA2', hue="cluster")
# Set the plot title
plt.title("Compounds Clustering - All Features")
# fig = px.scatter(pca_df, x='PCA1', y='PCA2', color="cluster", title="Compounds Clustering - 2 Components PCA", width=800)
# # saving the figure in html file
# # fig.write_html("cluster4.html")
# fig.show()
Explained variance ratio [0.78915463 0.21084537]
nSum of Explained variance ratio 0.9999999999999999
Columns chosen by PCA {'PC1': 'ALogP', 'PC2': 'ALogp2'}
Text(0.5, 1.0, 'Compounds Clustering - All Features')
# adding clusters to our data
clusters = pd.concat([without_compound, pd.DataFrame({'cluster': labels})], axis=1)
clusters.head(3)
| ALogP | ALogp2 | AMR | apol | nAcid | naAromAtom | nAromBond | nAtom | ATSc1 | ATSc2 | ... | WTPT-1 | WTPT-2 | WTPT-3 | WTPT-4 | WTPT-5 | WPATH | WPOL | XLogP | Zagreb | cluster | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -5.0931 | 25.939668 | 534.3813 | 317.363434 | 6 | 18 | 18 | 293 | 5.125258 | -2.388694 | ... | 306.516036 | 1.977523 | 153.543080 | 82.176355 | 71.366725 | 286334.0 | 230.0 | -8.623 | 752.0 | 1 |
| 1 | 0.4325 | 0.187056 | 317.0896 | 187.074612 | 0 | 20 | 21 | 171 | 2.269431 | -1.093604 | ... | 174.200943 | 2.002310 | 78.030370 | 30.548034 | 47.482336 | 46759.0 | 130.0 | 1.134 | 436.0 | 1 |
| 2 | -1.8743 | 3.513000 | 325.6625 | 190.878612 | 0 | 20 | 21 | 175 | 2.635543 | -1.251529 | ... | 181.809125 | 1.997902 | 88.729976 | 35.878826 | 52.851150 | 52357.0 | 134.0 | -2.012 | 458.0 | 1 |
3 rows × 223 columns
for c in clusters:
grid = sns.FacetGrid(clusters, col="cluster")
grid.map(plt.hist, c)
# for c in clusters:jupyter nbconvert --execute --to html clustering.ipynb
# grid = sns.FacetGrid(clusters, col="cluster")
# grid.map(sns.boxplot, c)